Enterprise credit evaluation and analysis method

By using an improved stacked autoencoder neural network model and a priori rule layer, combined with attention mechanism and domain knowledge, a corporate credit profile is generated, which solves the problems of insufficient evaluation accuracy and interpretability in traditional methods and achieves more accurate and reliable corporate credit assessment.

CN120807126APending Publication Date: 2025-10-17BEIJING YONGFENG AGRICULTURAL PORT SUPPLY CHAIN MANAGEMENT DEVELOPMENT CO LTD +1
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Patent Information

Application Number
CN202510797853.0
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-06-16
Publication Date
2025-10-17

AI Technical Summary

Technical Problem

Traditional corporate credit assessment methods are unable to fully explore the deep features and complex correlations in massive data, and lack the integration of domain knowledge, resulting in insufficient assessment accuracy and interpretability, and limited robustness.

Method used

An improved stacked autoencoder neural network model is introduced, combined with the attention layer and the prior rule layer. The feature weights are learned through the Scaled Dot-Product Attention mechanism, and domain knowledge is used to constrain features to generate corporate credit profiles.

Benefits of technology

It improves the accuracy and credibility of corporate credit assessment, enhances the interpretability of feature representation and the robustness of the model, and improves the adaptability and reliability of assessment results.

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Abstract

The invention discloses an enterprise credit evaluation analysis method, and relates to the field of data processing, and the method comprises the steps: obtaining the credit data of a plurality of to-be-evaluated enterprises; performing feature extraction on the credit investigation data through an improved stack type self-encoding neural network model to obtain feature data; the improved stack type self-encoding neural network model comprises an encoder, a decoder, an attention layer and a prior rule layer; the attention layer adopts a Scaled Dot-Product Attention mechanism to learn a weight feature matrix Ha, and the priori rule layer sets feature constraints according to a priori rule; clustering the feature data to obtain a cluster to which the enterprise belongs; according to the feature value distribution of the enterprises in the clustering clusters, performing feature scoring by using a prior rule to obtain clustering labels of the clustering clusters; according to the clustering label and the weight feature matrix Ha, generating an enterprise credit investigation portrait, and according to the enterprise credit investigation portrait, carrying out credit investigation evaluation analysis; aiming at the low enterprise credit evaluation precision in the prior art, the reliability of the evaluation result is improved.
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